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Higher and further education providers are facing the challenge of supporting the interaction needs of an increasing number of students who feature accessibility preferences to use both elearning contents and services. In the next future, we can expect that, within adaptive elearning systems, both automatic and manual procedures will interoperate to elicit users' interaction needs for ensuring accessibility. In this paper, we report findings from the user experience with the self‐assessment of interaction needs, as part of a content personalization system, which tackles possible mismatches in the interaction between the user and the learning objects. All stakeholders involved in providing this service along with intended user groups (students with visual, auditory or mobility impairments, and without impairments) participated in the evaluation with over 100 users described in this paper. From the evaluation, results follow that our approach allows students to self‐assess and report adequately their interaction preferences. Furthermore, the paper describes findings of interest and open issues about how massive online courses may address the accessibility needs of an increasing number of elearning users.

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Social online learning environments provide new recommendation opportunities to meet users' needs. However, current educational recommender systems do not usually take advantage of these opportunities. To progress on this issue, we have proposed a knowledge engineering approach based on human–computer interaction (i.e. user‐centred design as defined by the standard ISO 9241‐210:2010) and artificial intelligence techniques (i.e. data mining) that involve educators in the process of eliciting educational oriented recommendations. To date, this approach differs from most recommenders in education in focusing on identifying relevant actions to be recommended on e‐learning services from a user‐centric perspective, thus widening the range of recommendation types. This approach has been used to identify 32 recommendations that consider several types of actions, which focus on promoting active participation of learners and on strengthening the sharing of experiences among peers through the usage of the social services provided by the learning environment. The paper describes where data mining techniques have been applied to complement the user‐centred design methods to produce social oriented recommendations in online learning environments.